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Economic Research-Ekonomska Istraživanja
ISSN: 1331-677X (Print) 1848-9664 (Online) Journal homepage: http://www.tandfonline.com/loi/rero20
Return on strategic effectiveness – the need for
synchronising growth and development strategies
in the hotel industry using revenue management
Sonja Brlečić Valčić & Lidija Bagarić
To cite this article: Sonja Brlečić Valčić & Lidija Bagarić (2017) Return on strategic effectiveness
– the need for synchronising growth and development strategies in the hotel industry using
revenue management, Economic Research-Ekonomska Istraživanja, 30:1, 1631-1654, DOI:
10.1080/1331677X.2017.1383173
To link to this article: http://dx.doi.org/10.1080/1331677X.2017.1383173
© 2017 The Author(s). Published by Informa
UK Limited, trading as Taylor & Francis
Group
Published online: 09 Oct 2017.
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Download by: [University of Florida]
Date: 25 October 2017, At: 02:43
Economic Research-Ekonomska IstraŽivanja, 2017
VOL. 30, NO. 1, 1631–1654
https://doi.org/10.1080/1331677X.2017.1383173
OPEN ACCESS
Return on strategic effectiveness – the need for synchronising
growth and development strategies in the hotel industry
using revenue management
Sonja Brlečić Valčića and Lidija Bagarićb
Faculty of Economics, University of Rijeka, Rijeka, Croatia; bFaculty of Tourism & Hospitality Management,
Department of Marketing, University of Rijeka, Opatija, Croatia
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a
ABSTRACT
The purpose of this paper is to identify the essential determinants,
and highlight the importance, of synchronising the growth and
development strategies of hotel companies. The paper aims to analyse
preconditions to successful and stable business performance and
customer loyalty. The paper is based on the hypothesis that strategic
and organisational effectiveness helps to create preconditions
to stable business in the future, which is reflected in satisfactory
growth, financial strength and solvency, and results in creating
added value. The five-year financial data of three hotel companies
with similar business orientation from Istria County (Croatia) were
used to test the model for synchronising growth and development
strategies in the hotel industry and the fuzzy logic-based growthdevelopment synchronisation coefficient. The model was tested on
multi-annual results (the period 2010–2014), and conclusions and
recommendations were made for a future work.
ARTICLE HISTORY
Received 27 February 2016
Accepted 13 January 2017
KEYWORDS
Revenue management;
strategic effectiveness;
fuzzy model; pricing; hotel
management
JEL CLASSIFICATIONS
L21; L83; M31; D46
1. Introduction
The central interest of modern micro-economic analysis is often linked to issues relating to
the market behaviour of companies and, in turn, to the selection of business strategies. In
this context, business strategy selection is a vital determinant both in defining the company
and in establishing and forecasting value creation and preservation. Creating company
value is closely tied to long-term company equilibrium and expected business and financial
results. These must be viewed in light of the opportunities and threats as well as strengths
and weaknesses that a company could achieve through the action strategies it has selected.
Companies capable of creating and preserving value are most often companies that are
economically as well as socially sound, possess well-developed strategies and operations,
and have loyal clients. Many scientists agree that customer loyalty is a central concept of
marketing science (Aaker, 2002; Berzosa, Davila, & de Pablos Heredero, 2012; Jasinskas,
Streimikiene, Svagzdiene, & Simanavicius, 2016; Jones & Sasser, 1995; Kandampully &
CONTACT Sonja Brlečić Valčić sonja.brlecic@gmail.com
© 2017 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/
licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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1632 S. BRLEČIĆ VALČIĆ AND L. BAGARIĆ
Hu, 2007; Reichheld, 1993; Virvilaite, Piligrimiene, & Kliukaite, 2015). The importance of
loyalty is visible through the calculation that the attraction of new customers is much more
expensive than the retention of existing ones (Hallowell, 1996; Holmund & Kock, 1996; Kuo,
Chang, Cheng, & Lai, 2013; Reichheld & Sasser, 1990; Wong & Sohal, 2002).
Executional strategies and business policies are analysed to ensure they are aligned both
mutually and with the company’s mission and business goals, within the internal and external business environment. According to Diakoulakis, Georgopoulos, Koulouriotis, and
Emiris (2004) a competitive advantage can be achieved by firms that succeed by valuing
their intangible assets i.e., knowledge, technology and strategy, and by developing new
products and services. Metaxas, Koulouriotis, and Spartalis (2016) discussed the problem
of frameworks and systems that are based on the mechanistic view of management that sees
an organisation as a machine that produces money and not as organisations to transform
themselves when required.
Research conducted in recent years has indicated the need to examine the connections
between business operations, organisational structures, management, entrepreneurship
and strategies. As a result of such research, crucial links have emerged between corporate
entrepreneurship and entrepreneurial strategies (Urban, 2012). Chandler (1962) believes
that organisational structure follows strategy, whilst Porter (1998) emphasises that a company’s generic strategy generally translates into different organisational structures. These
views are echoed by Galbraith (2002) who identifies a link between a firm’s strategy and
organisational model. Raddats and Burton (2011) developed a new framework to show how
organisational structure changes in response to changes in strategy.
Although some authors refute Penrose’s (1959) classical theory of healthy growth, in
his book The Theory of the Growth of the Firm, it is undeniable that the strategies of today’s
companies are increasingly leaning towards this theory, which represents modern resourcebased thinking. Such thinking primarily refers to (1) the creation of competitive advantage,
(2) sustaining competitive advantage, (3) isolating mechanisms and (4) competitive advantage and economic rents (Rugman & Verbeke, 2002).
Revenue management has emerged from the framework of similar thinking. A systems
process designed to increase revenue with regard to demand, reservation distribution and
changing prices, revenue management involves an analytical process for predicting customer
behaviour at the micro market level. Considering that this foremost refers to action taken
to understand clients and their perceptions of product or service value and, accordingly,
to harmonising product prices, sales and availability to each business segment, revenue
management has found its application largely in the hotel industry. Hence, revenue management combines data mining and operational research with strategy, primarily for the
purpose of understanding customer behaviour.
Therefore, it may be said that good revenue management is a crucial precondition to the
Strategic Benchmarking for Value Process, which is the backbone of the system for creating
and enhancing customer satisfaction. Satisfied customers tend to buy products and services
on a lasting basis, thus providing a flow of revenue which the management can transform
into profit, free cash flow or use to attract investors.
Strategic and organisational effectiveness provides the preconditions to stable business
operations in the future, which are reflected in satisfactory growth, financial strength and
solvency, and in the creation of added value.
ECONOMIC RESEARCH-EKONOMSKA ISTRAŽIVANJA 1633
The purpose of this paper is to define the crucial determinants and importance of synchronising the growth and development strategies of hotel companies, in the context of
strategic effectiveness in achieving positive business and financial results in the future.
Parameters were selected based on theoretical background and a coefficient of synchronised hotel company growth and development was created based on a fuzzy model. The
model was tested on hotel companies based on multi-annual results, and conclusions and
recommendations were made for future work.
2. Theoretical background
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2.1. Correlation between a company’s development and growth strategies
In the given economic framework, company development refers to the process of transition from an existing state to a new state, which should be more effective as it brings many
changes in all areas of business. This foremost involves increasing the value of production
output and improving technology and organisation. In addition, business efficiency often
requires the development of new departments or the reorganisation of old ones, the expansion of product or service programmes, the improvement of working conditions and the
improvement of overall business organisation.
Such qualitative changes in a company which lead to changes in the structure of future
functionality and overall performance are usually the result of adjustments to market conditions. The changes refer to the suitability of company operations relative to involvement in
different markets and to the company’s internal structure and optimal production output. In
this way a company adapts its internal capabilities to market opportunities. The inevitability
of change in terms of company development is a reaction to change that is constantly occurring on the market and the primary objective of which is survival. Through development,
the company ties its structure in with all the effects of production output, and adapts to
emerging market conditions through its line of products and services. Change results in a
new relationship with the environment, the outcome of which should be new potential for
the company. Positive change occurs when the company is at a higher level of development,
which is reflected in enhanced results relative to the environment (for example, relative to
a branch of industry or economic activity), while a lower level of development is reflected
in results that are poorer than expected.
Econometric analyses of companies at various levels of total factor productivity have
shown that companies capable of improving the effectiveness of their business processes
in a specific period of time have a greater probability of maintaining above-average output
in later time periods (Antonelli, Crespi, & Scellato, 2015).
Today, managers are overly concerned with managing current and short-term earnings,
while almost completely neglecting to foster the learning organisation and culture that stable
growth requires (Laurie & Harreld, 2013). Six common failings occur: the absence of the
right type of supervision that would let managers be free to focus on team development and
potential clients, the failure to put the best and most skilled staff in management positions,
the building of an unsuitable team in terms of capabilities and goals aimed at a business
model oriented towards a value creation and preservation strategy, the wrong approach to
assessing success with regard to putting in place and monitoring plans, the wrong way of
managing finances, and the failure to tap into the core competencies of the organisation
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1634 S. BRLEČIĆ VALČIĆ AND L. BAGARIĆ
in which the Chief Executive Officer (CEO) should play a leading role in initiating growth
within the framework of available resources (Laurie & Harreld, 2013).
Management strategies are crucial in shaping the volume of knowledge needed to create and maintain total factor productivity (TFP), a vital factor of business development
(Antonelli et al., 2015). TFP largely depends on the amount of investment made in research
and development (R&D), but also on decisions that are made with regard to the acquisition
of specific knowledge. In this respect, research into organisational economics has shown
that repeated interaction exists between knowledge accumulation and creating routines;
this interaction should be valorised and used in organisational purposes to help create
dynamic opportunities that facilitate systematic reliance on innovation as a competitive
tool (Antonelli et al., 2015).
Business growth strategy by basic definition has the intention to win a larger market share
by determination of the target market, design of a detailed set of activities and development
of efficient customer channels. In focus of recent theories, growth strategy has to be focused
on enhancing customer value by efficient customer management and expanding revenue
opportunities (Kaplan & Northon, 2005). Therefore, company growth is a result, as well as a
measure, of company development. It refers to a quantitative increase in the volume of product/service production, foremost, through the introduction of new production or service
capacities with no change to the existing structure. In addition to a quantitative increase in
the volume of business, development also implies a qualitative increase, improvement and
innovations to the existing business. Sustainable growth comes from the entire company,
not only from some particular product or service (Leinwand & Mainardi, 2016). It can be
concluded that growth represents a quantitative component of change; and development,
a qualitative one.
There are many studies concerning the correlation between core competencies, skills,
performance and corporate growth. The values and behaviours that contribute to the unique
social and psychological environment of an organisation as well as excellent team management are the basics for successfully carrying out core competencies and skills. Hence it is
necessary to (Yang, 2015):
• define core competencies and skills,
• define the ‘core product or service’,
• determine the relationship between core competencies and skills,
• adjust the core competencies, and
• put in place a strategic process for managing core competencies.
High growth expectations and the accordingly implemented strategies are mostly unsustainable in the mid-term period. Such growth expectation could be damaging to the business
(Groucutt, 2007). Successful companies possess business strategies for developing new
technologies, entering new markets, creating new jobs and cultivating an innovative culture.
Identifying core competencies and efficiently combining competencies with core skills are
vital strategic activities in gaining returns in the long run (Yang, 2015).
Businesses which engage in strategic planning are more likely to achieve higher sales
growth, return on assets, profit and employee growth (Berman, Gordon, & Sussman,
1997; Carland & Carland, 2003; Gibson & Casser, 2005; Mitchelmore & Rowley, 2013;
Wijewardena, De Zoysa, Fonseka, & Perera, 2004). Mazzarol, Reboud, and Soutar (2009)
found that firms that possessed formal written business plans were found to be more likely
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ECONOMIC RESEARCH-EKONOMSKA ISTRAŽIVANJA 1635
to have stronger support network partnerships, formal quality assurance and the ability to
lead change among employees. Some studies have used annual sales, number of employees,
return on sales, growth in sales and growth in employee numbers (Brush & Vanderwerf,
1992).
A quantitative increase of business volume serves the fundamental goal of company
growth and that is to increase returns. However, in addition to being seen as the scope of
growth (the growth of dimensions), the category of company growth can also be viewed
through the growth of sales, the growth of business quality, the growth of a company’s power
and strength, and the growth of company value.
The drivers of growth in all the categories mentioned can be divided into internal and
external drivers. Internal drivers are factors of improved utilisation of production or service
capacities, through better, more rational, more effective and more efficient usage, while
external drivers are usually capital investments, the most important and most common
source of growth.
2.2. Revenue management in the Strategic Benchmarking for Value context
The starting points of the Strategic Benchmarking for Value (SBV) process are the increase
of free cash flow and return on equity (ROE), essential determinants of value creation.
Primarily, this process involves identifying business strategies, critical success factors and
related key performance indicators; setting up a strategic benchmarking system; aligning
company goals with the goals of strategic analysis; and then monitoring and adjusting value
creation and preservation models and procedures.
Organisational effectiveness can only be measured by observing quantitative success
factors that are tied to qualitative factors. Qualitative factors determine the value, purpose,
meaning and vision of a company. In this context, strategic thinking and leadership play
prominent roles in creating and running an organisation. Hence, for managers to efficiently
manage resources, it is vital they identify and define basic objectives (Fairholm & Card,
2009).
Return on strategic effectiveness (ROSE) can be said to depend on productivity and
growth. Productivity is the outcome of development and is measured by an increase in
effectiveness (profitability), by an improvement in asset turnover and by capital structure
(leverage). An increase in market share and the introduction of new products and services
should lead to growth, which is measured by the increase of cash flows. Growth will enhance
profitability only if it is aligned with a company’s unique resources and competencies (Urban,
2012). In the hotel industry, ROSE primarily depends on revenue management effectiveness.
As demand patterns are becoming increasingly unpredictable and dependent on user-generated contents, the most important dynamic variables of Hotel Revenue Management are
reviews, guest satisfaction and prices. Hence, at the heart of revenue management rests the
demand concept based on changing prices (Choi & Mattila, 2004).
What makes hotels suitable to be able to apply Revenue Management (www.xotels.com):
• fixed capacity,
• perishable product,
• high fixed costs and low variable costs,
• product can be priced differently,
1636 S. BRLEČIĆ VALČIĆ AND L. BAGARIĆ
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• demand evolves,
• product can be sold in advance, and
• market can be segmented.
A successful revenue management is based on efficiently controlling customer demands.
Two interconnected strategic leverages a hotel company can apply to that end are pricing
and duration of customer use. Prices can be fixed (one price for the same service for all
customers for all time) or variable (different prices for different times and different customer
segments), while duration is predictable or unpredictable. Using variable prices to control
demand is conceptually a simple process that is applied in the form of various discounts
on prices for all customers or for individual classes of customers (Noonea, McGuireb, &
Rohlfsc, 2011). Effective revenue management not only maximises revenue in periods of
high demand, but also helps to stimulate demand in periods of low demand, without the
need for large deviations in prices. Hence, revenue management in the hotel industry should
be a long-term strategic tool for growth and development, within the context of generating
more revenue and higher profitability in the future.
2.3. Fuzzy sets applied to solving economic problems
Solving economic problems involves not only measuring and monitoring specific phenomena in a given period, but also calls for a type of thinking based on the experience
of different kinds of experts. In seeking and designing models for solving problems of an
economic nature, logical solutions can be found not only by combining numerical and
non-numerical information but also by using models adapted to the human mind-set.
The mathematical theory of fuzzy subsets makes it possible to include subjectivity and/or
uncertainty in seeking objectivity.
A fuzzy set is used to describe an input space to an output space, firstly introduced by
L.A. Zadeh (1965) the theory for fuzzy sets in an attempt to deal with fuzzy situations of
the real world like uncertainty, vagueness and incompleteness. Buckley (1985) incorporated
the fuzzy set theory into the traditional analytical hierarchy process and became a suitable
tool for solving real-world multi-criteria decision-making problems (Büyüközkan, 2004;
Fu, Chu, Chao, Lee, & Liao, 2011; Gil-Lafuente, 2005; Huang & Wu, 2005; Jeng & Bailey,
2012; Lin, Lee, & Chen, 2009; Merigó & Gil-Lafuente, 2010; Sipahi & Timor, 2010). It has
been used in the fields of service and tourism (Chen & Wang, 2010; Cho & Lee, 2013; GilLafuente, Merigó, & Vizuete, 2014; Petrovic-Lazarevic & Wong, 2000; Wang & Durugbo,
2013).
Fuzzy logic has two different meanings. In a narrower sense, it is a logic system that is an
extension of binary logic, and in a broader sense, it is practically synonymous to the fuzzy
set theory. The starting point of fuzzy logic is mapping input space to an output space (The
MathWorks, 2010).
The fuzzy set A = {x, A (x) | x ∈ X} is a set without clearly and precisely defined
boundaries, meaning it can also contain the elements x ∊ X that have only a partial degree
of membership to set A. Membership is described by the membership function (MF) μA(x)
with a range covering the interval [0, 1]. In other words, a membership function is a curve
that defines how the elements of input set X are mapped to a specific degree of membership.
Although there are a great number of different membership functions, two very frequently
applied membership functions were used for the purpose of this paper (Figure 1).
ECONOMIC RESEARCH-EKONOMSKA ISTRAŽIVANJA 1637
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Figure 1. Generalised bell and triangular membership functions. Source: Brlečić Valčić (2014).
These are:
• The generalised bell membership function (gbellmf) expressed as
f (x) =
1
1 + |(x − c)∕a|2b
(1)
is dependent upon coefficients a, b and c, where generally b > 0, and c defines the centre
of the curve;
• The triangular membership function (trimf) is expressed as
⎧
⎪ 0,
⎪ x − a,
⎪ −a
f (x) = ⎨ cb −
x
,
⎪
⎪ c−b
⎪ 0,
⎩
⎫
⎪
a≤x≤b⎪
⎪
⎬
b≤x≤c ⎪
⎪
c≤x
⎪
⎭
x≤a
(2)
where parameters a and c allocate the points of the triangular function on the x-axis, while
parameter b allocates the peak of the triangle.
Fuzzy sets are a basis for fuzzy theory. In inference, most commonly used are mathematical process descriptions with linguistic rules expressed as:
IF (x is LVx) THEN (y is LVy)
(3)
where LVx and LVy are the values of linguistic variables defined by fuzzy sets over a range of
values of input sets X and Y, respectively. The value range of an input or output parameter
is defined by an interval from its lowest to its highest value (Brlečić Valčić, 2014).
Each input and output variable has to be defined by a corresponding number of linguistic
variables that describe the features of a given variable. For the purpose of this paper, an
equal number of linguistic variables is assumed for all input and output variables. In this
case, linguistic variables can be said to belong to the set {LV1, LV2, ..., LVm}.
1638 S. BRLEČIĆ VALČIĆ AND L. BAGARIĆ
When several output variables are used to design a fuzzy inference system (FIS), they
can be interconnected by different fuzzy operators such as AND (intersection), OR (union),
NOT, etc. If the IF-THEN rules are to be applied to interconnect all possible combinations
that can be made from n input variables, one output variable and m linguistic variables,
then only the AND operator can be used and mn rules must be realised.
In general form, any IF-THEN rule of a fuzzy inference system with n input variables
(x1, x2, ..., xn), m linguistic variables and one output variable (y) can be expressed using the
AND operator, as follows
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IF (x1 is LV1 ) AND (x2 is LV2 ) AND … AND (xn is LVn ) THEN (y is LVy )
(4)
where the linguistic values LV1, LV2, …, LVn belong to the previously defined set
{LV1, LV2, ..., LVm}, while LVy is the linguistic variable that describes some concrete state of
the output variable (Brlečić Valčić, 2014).
Unlike in classical Boolean logic, in fuzzy theory the AND operator (intersection) may
be generalised using a number of different procedures such as algebraic product, algebraic
sum, bounded sum, bounded difference, etc. In this model, the AND operator is realised in
the minimisation procedure. In other words, if a fuzzy rule connects two fuzzy sets A and
B using the AND operator, the degree of membership of their intersection C is obtained
by the expression
C = min(A , B ).
(5)
In general, whenever a fuzzy model is developed, an FIS is created. From a practical viewpoint, the entire procedure as illustrated in Figure 2 consists of several steps:
• select the input and output variables, give them names and define their value ranges,
• describe input and output variables using linguistic values,
• select (type, number) and model membership functions (MF) for each input and
output variable,
• determine the required fuzzy IF-THEN rules to connect input and output variables
with linguistic values, and apply fuzzy operators and implication methods, and
• defuzzify.
Figure 2. Simplified presentation of the FIS creation process and the mutual interaction of process phases.
Source: Brlečić Valčić (2014).
ECONOMIC RESEARCH-EKONOMSKA ISTRAŽIVANJA 1639
Because the final decision, that is, FIS output is based on testing all IF-THEN rules defined,
it follows that the outputs must be combined and taken jointly into consideration. This is the
aggregation phase. In the aggregation process, fuzzy sets representing the outputs of each
individual rule are grouped into one joint fuzzy set. While several methods are available
for this process, the maximising method has been used in this paper.
Defuzzification is carried out to obtain defuzzified FIS output variable values. The most
commonly used defuzzification method is the centroid method. This method determines
the coordinates of the centre of gravity T(y0, μ0) of the area below the curve of aggregated
membership function μ(y) relative to the range of output variable y, where the coordinate
y0 is the defuzzified output value.
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3. Methodology
3.1. Defining the concept of the growth-development synchronisation coefficient
Quantitative measures of business activities are typical of the production industries. To
assess the quality of revenue in the hotel industry, however, this paper uses value or financial measures.
The first measure and also the first parameter in determining the coefficient of growth-development synchronisation (G&D coefficient) is the asset turnover coefficient, which measures the intensity of business activities with which a company uses its assets (Belak, 2014).
A comparison of this indicator within an industry and, especially, in hotels operating in
similar conditions (similar destinations, similar visitor numbers in the region, etc.) provides a good illustration of the efficiency of assets usage. If a company is able to increase
this coefficient, in most cases it is likely to improve its overall performance and balance
structure. The coefficient is expressed as:
Asset turnover coefficient = (sales revenue)∕(total asset).
(6)
The control measure of the asset turnover coefficient is not a conventional control measure,
but rather is derived from the average of the industry based on experiential relationships in
the economy which performs well in its totality (Belak, 2014). For a company to be considered sound, the value of this coefficient should be at least 1. The value varies by industries
and, for example, amounts to 1 in production industries, 2 in commerce and 3–9 in intellectual services. Unsatisfactory value of this ratio may indicate the need for improvement
in the area of working capital management and management of long-term assets (Palepu
& Healy, 2008) as well as the area of human resource effectiveness (Helfert, 2005) and the
area of effective revenue management (Noonea et al., 2011).
The EBITDA/asset ratio was the second parameter selected to determine the G&D coefficient. Because earnings is a complex concept, and net profit results from the effects of
different categories of income and expenditure, and gain and loss, these measures can be
fully understood only when they have been examined from a number of perspectives. Ratio
can indicate the need for improvements in business processes regarding asset utilisation,
operating processes and operating expenses management (Helfert, 2005). To be a useful
measure of earnings, EDITBA relies on its control measure. For calculations in this paper,
EDITBA/asset, one of several comparative ratios, was used. The general control measure
for EDITBA/asset is 13.50% (Belak, 2014).
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1640 S. BRLEČIĆ VALČIĆ AND L. BAGARIĆ
The third parameter used in determining the G&D coefficient was net profit over equity
capital. Because the sum of net profit and depreciation in five years is expected to earn
the entire equity capital in typical business operations, the expected net profit over capital
amounts to 12% (Belak, 2014). This ratio can indicate the need for improvements regarding
adjustments to industry conditions, competitive strategy analysis, operating management,
investment management and liability management (Palepu & Healy, 2008).
Business Excellence was selected as the fourth parameter in determining the G&D coefficient. Aimed at creating value for owners, Business Excellence is a managerial system and
organisational process of strengthening, developing and improving business performance.
The system focuses on different areas within a company’s organisation, ranging from management and client orientation to managing information, people and business processes,
to achieve superior performance (Brlečić Valčić, 2014; Metaxas et al., 2016). Because of
its proven quality, the BEX (Business Excellence) Index has been selected to determine
business excellence in this paper.
The BEX Index is defined by four influence-weighted indicators according to the following expression (Belak, 2014):
BEX = 0.388 × ex1 + 0.579 × ex2 + 0.153 × ex3 + 0.316 × ex4 ,
(7)
where ex1 is the profitability indicator; ex2 is the value creation indicator; ex3 is the liquidity
indicator; and ex4 is the financial strength indicator, defined as follows:
ex1 = EBIT∕(total asset),
(8)
ex2 = (income after tax)∕(equity × price),
(9)
ex3 = (working capital)∕(total assets),
(10)
ex4 = 5 × (income + depreciation + amortisation)∕(total liabilities).
(11)
According to BEX Index values, companies fall into the following categories:
• good companies with a BEX Index higher than 1,
• companies with a BEX Index between 0 and 1, in need of improvements in their
business operations, and
• companies with a BEX Index lower that 0, whose existence is threatened.
The final parameter for determining the G&D coefficient is value for money. Not only is
this indicator based on minimum purchase price as an economic category but it also signals maximum purchase effectiveness and efficiency (www.businessdictionary.com) and
is, therefore, a crucial factor in assessing consumer satisfaction, a vital category of future
revenue generation. The source of the indicator for the needs of this paper was the service
www.booking.com.
The reference values as well as control and corrective measures of input parameters in
the model for calculating the G&D coefficient are presented in Table 1.
This enables the easy interpretation of results derived using the model proposed in
this paper as well as the implementation of corrective measures in case of unsatisfactory
reference values.
ECONOMIC RESEARCH-EKONOMSKA ISTRAŽIVANJA 1641
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Table 1. Reference values of input variables with control and corrective measures.
Indicator
Asset turnover
Reference value
> 1.00
≤ 1.00
EBITDA / asset
> 13.50 %
≤ 13.50 %
Net income /
equity
≥ 12.00 %
< 12.00 %
BEX
≥ 4.00
(≥ 2.00) & (< 4.00)
(≥ 1.00) & (< 2.00)
< 1.00
Value for
money
Frequency counts
(numbers and percentages)
Control / corrective measure
Comparison with similar companies and planned Asset turnover.
Significant improvements of business processes are required particularly in:
revenue management, cost leadership strategy, productive asset utilisation,
investment management, working capital management and management
of long-term assets.
Comparison with similar companies and planned EBITDA / asset.
Improvements of business processes are required particularly in effective
asset utilisation, operating processes and operating expenses management.
Comparison with similar companies and planned Net income / equity.
Additional improvements in terms of adjustments to industry conditions,
competitive strategy analysis, operating management, investment management and liability management.
Investments in business processes are satisfactory.
Additional investments in business processes are required.
Additional investments in business processes are required, particularly in leadership excellence, production and supply chain, strategic agility, continuous
improvement, partnerships and intellectual capital.
Significant investments in business processes are required, particularly in
leadership excellence, production and supply chain, strategic agility, continuous improvement, partnerships and intellectual capital.
Upgrading of customer needs in order to improve customer satisfaction.
Source: Authors.
3.2. Developing the growth-development synchronisation coefficient based on
fuzzy logic
The following parameters with related values were applied to a Fuzzy Inference System to
develop the G&D coefficient:
• Asset turnover coefficient with related range [0, 5],
• EBITDA/asset with related range [0, 0.5],
• Net income over equity capital with related range [-0.05, 0.3],
• Business Excellence with related range [-2, 8],
• Value for money with related range [1, 10], and
• G&D coefficient output parameter with related range [1, 5].
The reference values as well as control and corrective measures regarding the output parameter of the model, i.e., the G&D coefficient, are presented in Table 2.
Each input and output variable was described using the linguistic variable values from
the following set:
{very low (VL), low(L), medium (M), high (H), very high (VH)}.
Because only the AND operator was used in creating the FIS, for defining all fuzzy rule
combinations with five input parameters and their five associated linguistic values, a total
of 55 = 3125 IF-THEN rules were required. In addition to this concept, the Technique for
Order Preference by Similarity to Ideal Situation (TOPSIS) was used to evaluate multiple
alternatives against the selected criteria (Saghafian & Hejazi, 2005).
In general form, any IF-THEN rule of a FIS with five input variables and one output
variable can be expressed by the AND operator as follows:
1642 S. BRLEČIĆ VALČIĆ AND L. BAGARIĆ
IF (x1 is a) AND (x2 is b) AND (x3 is c) ...
}
AND (x4 is d) AND (x5 is e) THEN (y is f )
(12)
where all linguistic variables belong to set {(VL), (L), (M), (H), (VH)}, i.e., to its equivalent
form {1, 2, 3, 4, 5}.
For the needs of this model, the IF-THEN rules have not been additionally weighted.
Therefore, the indexed form of an arbitrary IF-THEN rule in the FIS structure for developing
the G&D coefficient can be expressed in Matlab notation (The MathWorks, 2010) as follows:
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a b c d e, f (w) :r
(13)
where {a, b, c, d, e} and f are linguistic variable values of input and output parameters,
respectively, w ∊ [0, 1] indicates the weight applied to each rule and r indicates whether an
AND (1) or an OR (2) rule is applied. Moreover, the output value f according to TOPSIS
approach is determined as the mean value of input linguistic variable values a, b, c, d and e
rounded to the nearest integer. For example, a small, but illustrative number of 3125 fuzzy
rules are shown in Table 3.
Table 2. Reference values of the G&D coefficient with associated corrective measures.
Indicator
G&D coefficient
Reference value
≥ 4.00
(≥ 2.00) & (< 4.00)
(≥ 1.00) & (< 2.00)
< 1.00
Control / corrective measures
Growth and development business strategy is satisfactory.
Additional improvements in customer management both with expanding of
revenue opportunities are required.
Additional improvements in determination of competencies and skills, core
products or services, and the relationship between core competencies and
skills are required. Adjustment of the core competencies is needed with
putting in place a strategic process for managing core competencies.
Significant improvements in determination of competencies and skills, core
products or services, and the relationship between core competencies and
skills are required. Adjustment of the core competencies is needed with
putting in place a strategic process for managing core competencies.
Source: Authors.
Table 3. Truncated list of 3125 fuzzy rules within TOPSIS approach with five inputs and one output.
Rule #
1.
2.
3.
⋮
111.
112.
113.
⋮
996.
997.
998.
⋮
3123.
3124.
3125.
Source: Authors.
Indexed form of IF-THEN rule
5 5 5 5 5, 5 (1) : 1
5 5 5 5 4, 5 (1) : 1
5 5 5 5 3, 5 (1) : 1
⋮
5 5 1 3 5, 4 (1) : 1
5 5 1 3 4, 4 (1) : 1
5 5 1 3 3, 3 (1) : 1
⋮
4 3 1 1 5, 3 (1) : 1
4 3 1 1 4, 3 (1) : 1
4 3 1 1 3, 2 (1) : 1
⋮
1 1 1 1 3, 1 (1) : 1
1 1 1 1 2, 1 (1) : 1
1 1 1 1 1, 1 (1) : 1
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ECONOMIC RESEARCH-EKONOMSKA ISTRAŽIVANJA 1643
The FIS architecture for developing, i.e., fuzzy modelling of the G&D coefficient in
terms of five predefined inputs (asset turnover, EBITDA / asset, net income / capital, BEX
index, value for money), 3125 fuzzy TOPSIS based rules and one output (G&D coefficient)
is shown in Figure 3.
The geometry of the membership function curve of input variables was selected to ensure
that the linguistic variables are mapped as representatively as possible on to the defined range
of values, in particular where the interpretation of the value of individual input variables
is concerned. Generalised bell membership functions (1) were used for input variables
EBITDA / asset, net income / capital and BEX index, while triangular membership functions
(2) were used for input variables asset turnover and value for money as well as for the output
variable G&D coefficient. Parameter values (a, b, c) of used membership functions (1) and
(2) are presented in Table 4. The centroid method was used in the defuzzification process.
The 3D visualisations of surfaces which present the mapping of two selected input parameters on to the output parameter are presented in Figures 4 and 5.
Figure 3. FIS structure for fuzzy modelling of the G&D coefficient. Source: Authors.
Table 4. MF parameter values (a, b, c) of input and output variables.
Variable name
Asset turnover
Variable
type
Input
Variable
range
[0, 5]
Type of MF
trimf
EBITDA / asset
Input
[0, 0.5]
gbellmf
Net income/
capital
Input
[-0.05, 0.3]
gbellmf
BEX index
Input
[-2, 8]
gbellmf
Value for money
Input
[1, 10]
trimf
G&D coefficient
Output
[1, 5]
trimf
Source: Authors.
Values of MF parameters (a, b, c) with respect to
linguistic variables (VL, L, M, H, VH)
VL (-1.25, 0, 0.3333), L (0, 0.3889, 1), M (0.4444, 0.8611,
1.667), H (0.7778, 1.5, 3), VH (1.3, 3, 1000)
VL (0.105, 7.47, −0.0622), L (0.0348, 1.064, 0.062), M
(0.03823, 1.24, 0.109), H (0.0407, 1.14, 0.1617), VH
(0.216, 5.39, 0.3806)
VL (0.0735, 5.23, −0.0586), L (0.0244, 1.06, 0.0332),
M (0.0222, 1.24, 0.0694), H (0.0225, 1.14, 0.106), VH
(0.151, 5.39, 0.2636)
VL (2.1, 3.81, −2), L (0.6968, 1.38, 0.8), M (0.746, 1.24,
1.844), H (1.29, 1.65, 3.48), VH (2.76, 3.24, 6.836)
VL (-1.25, 1, 3.25), L (1, 3.25, 5.5), M (3.25, 5.5, 7.75), H
(5.5, 7.75, 10), VH (7.75, 10, 12.25)
VL (0, 1, 2), L (1, 2, 3), M (2, 3, 4), H (3, 4, 5), VH (4, 5, 6)
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1644 S. BRLEČIĆ VALČIĆ AND L. BAGARIĆ
Figure 4. Surface visualisation of G&D coefficient in terms of EBITDA/Asset ratio and Asset turnover.
Source: Authors.
Figure 5. Surface visualisation of G&D coefficient in terms of BEX Index and Asset turnover. Source: Authors.
Figure 4 shows the visualisation of G&D coefficient surface in terms of EBITDA/Asset
and Asset turnover, and Figure 5 shows the output, i.e., G&D coefficient in terms of the
BEX Index and Asset turnover.
3.3. Design of a conceptual model for synchronising growth and development
strategies in the hotel industry
In order to ensure enhanced business efficiency in the future, a conceptual model for monitoring the synchronisation of growth and development strategies is proposed. The simplified
structure of this model is presented in Figure 6.
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ECONOMIC RESEARCH-EKONOMSKA ISTRAŽIVANJA 1645
Figure 6. Proposed conceptual model for monitoring the synchronisation of growth and development
strategies. Source: Authors.
The basic step of the proposed conceptual model involves analysis of a company’s financial parameters during the five-year period and comparison with its close competitors in the
business environment under similar business conditions. This mainly refers to monitoring
changes in the categories of revenue, EBITDA and profit, as well as in other categories such
as added value, operational cash flows, financial strength and business excellence.
Calculating the growth-development synchronisation coefficient (G&D coefficient) based
on a fuzzy model provides information concerning the need to invest in efficient revenue
management and in business organisation in order to create added value.
Added value creation is a key platform for business efficiency. By properly managing this
category a company can ensure multiple beneficial effects in business operations such as an
increase in its product/service prices, setting itself apart from its competitors, protecting
itself against competition with the possibility of reducing prices, having a stronger focus
on business through target market segments and so on.
4. Results and discussion
Five-year financial data for the period 2010–2014 of three Croatian hotel companies (A,
B and C) from Istria County were used to test the model for synchronising growth and
development strategies in the hotel industry and the fuzzy logic based growth-development
synchronisation coefficient.
Data were drawn from the Bisnode – Poslovna Hrvatska database (www.poslovna.hr).
Data pertaining to the value-for-money parameter were drawn from the Internet portal
1646 S. BRLEČIĆ VALČIĆ AND L. BAGARIĆ
Booking.com (www.booking.com) as the average score of all rated hotels within a given
hotel company.
The calculation of financial parameters: Revenue, EBITDA, Added Value, Cash from
Operation (CFO), Gross Profit (P/L gross), Net Profit (P/L net), Financial Strength (FS)
and Business Excellence (BEX), that are presented in Table 5 and shown in Figures 7, 8 and
9, makes it possible to compare trends, as illustrated in the proposed conceptual model for
synchronising growth and development strategies in the hotel industry (Figure 6).
Table 5. Financial parameters for trend analysis in five-year period (2010–2014).
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Financial parameters (in mil. Kn)
Hotel Co.
A
B
C
Year
2010
2011
2012
2013
2014
2010
2011
2012
2013
2014
2010
2011
2012
2013
2014
Revenue
505.59
581.08
643.99
690.81
744.71
440.20
522.66
745.11
806.40
1065.15
369.02
403.57
425.15
440.83
425.61
EBITDA
149.05
164.07
217.12
256.77
294.78
120.89
129.53
208.61
207.81
232.19
118.48
129.40
130.98
135.95
127.20
Added Value
347.49
386.78
473.27
497.61
557.48
296.66
348.69
500.50
518.76
668.55
249.60
275.69
283.84
293.27
289.19
CFO
108.94
110.88
178.70
200.83
230.51
86.68
115.19
213.38
235.50
211.25
27.11
77.63
105.03
129.09
107.21
P/L gross
−13.68
−10.13
52.01
83.66
123.91
−12.85
0.07
54.03
35.12
27.27
−34.22
28.23
61.45
71.41
45.48
Source: Authors.
Figure 7. Financial parameters for Hotel Company A. Source: Authors.
Ratios
P/L net
−13.16
−8.45
55.14
75.02
99.02
−12.85
1.79
52.67
58.65
23.63
−28.35
22.32
48.76
71.41
45.48
FS
0.50
0.50
0.91
1.08
1.14
1.06
1.39
2.52
1.23
1.01
0.24
0.70
0.99
1.23
1.11
BEX
−0.11
−0.04
0.99
1.24
1.50
0.17
0.46
1.26
0.84
0.49
−0.53
0.66
1.23
1.62
1.09
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ECONOMIC RESEARCH-EKONOMSKA ISTRAŽIVANJA 1647
Figure 8. Financial parameters for Hotel Company B. Source: Authors.
Figure 9. Financial parameters for Hotel Company C. Source: Authors.
In the period 2010–2014, the parameters Revenue, EBITDA, Added Value and Cash from
Operation (CFO) of the hotel company A all display the same trend. The parameters Gross
Profit (P/L gross) and Net Profit (P/L net), Financial Strength (FS) and BEX, however, do
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1648 S. BRLEČIĆ VALČIĆ AND L. BAGARIĆ
not fully match this trend. The trends of Profit and Financial Strength are more similar to
the trends of Business Excellence and Revenue, respectively. Only in the period 2010–2011
do all examined categories show similar trends.
The hotel company B also recorded similar trends across all categories in the period
2010–2011. In the period 2011–2012 the trend of Financial Strength approximately matched
the trends of Profit and Revenue, while the trend of the Business Excellence parameter
was closer to the trend of Added Value. In the period 2012–2014, the trends by category
completely diverge. For that period, the parameters Revenue and EBITDA, Added Value
and CFO, Gross Profit, Financial Strength and Excellence could be grouped into the same
categories.
Similar trends are observed in the period 2010–2014 in the parameters Revenue, EBITDA,
Added Value and CFO of the hotel company C. In that period, the parameters Gross and
Net Profit, Financial Strength and Excellence show a similar trend, which differs from the
trend of the categories Revenue, EBITDA, Added Value and CFO.
The values of input variables for calculation of fuzzy logic-based growth-development
synchronisation coefficient in five-year period (2010–2014) for observed hotel companies
A, B and C are given in Table 6.
Based on shown input variables one can conclude that all observed hotel companies
need improvements and additional investment in business processes (Table 1). However,
the results of continuous increase over the years in all analysed indicators of the hotel company A indicate constant investments in the development of business processes. In terms
of need for additional improvements, determined by unsatisfactory benchmark of Asset
turnover ratio, they are expected in two primary areas of asset management: working capital management and management of long-term assets (Palepu & Healy, 2008). Deviations
from the reference values also indicate the need for improvement in the area of human
resource effectiveness (Helfert, 2005) as well as in the area of effective revenue management
that for example can be achieved by using variable prices to control demand (Noonea et
al., 2011). Insufficient values of EBITDA / asset indicator show the need for improvements
in business processes regarding asset utilisation, operating processes and management of
operating expenses (Helfert, 2005). The need for larger additional improvements in terms
Table 6. Values of input variables in five-year period (2010–2014) for hotel companies A, B and C.
Hotel Co.
A
B
C
Year
2010
2011
2012
2013
2014
2010
2011
2012
2013
2014
2010
2011
2012
2013
2014
Source: Authors.
Asset turnover
0.2702
0.2969
0.3438
0.3472
0.3631
0.2748
0.2520
0.3565
0.2846
0.3442
0.3020
0.3223
0.3381
0.3341
0.3208
EBITDA / asset
0.0761
0.0796
0.1077
0.1240
0.1325
0.0726
0.0605
0.0961
0.0702
0.0727
0.0940
0.1009
0.1006
0.0991
0.0923
Net income /
capital
−0.0135
−0.0086
0.0526
0.0658
0.0816
−0.0103
0.0011
0.0307
0.0300
0.0114
−0.0402
0.0307
0.0628
0.0843
0.0509
BEX index
−0.1129
−0.0450
0.9906
1.2442
1.4960
0.1725
0.4595
1.2560
0.8397
0.4886
−0.5276
0.6615
1.2294
1.6178
1.0891
Value for money
8.0187
7.9106
7.7032
7.9714
8.1021
7.9000
7.8970
7.9871
8.0150
7.8454
7.1719
7.4012
7.3784
7.5614
7.6871
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ECONOMIC RESEARCH-EKONOMSKA ISTRAŽIVANJA 1649
of adjustments to industry conditions, competitive strategy analysis, operating management, investment management and liability management, shown by Net income / equity
ratio (Palepu & Healy, 2008), is required in hotel companies B and C, while the values of
the same ratio indicate the necessity for some minor improvements in hotel company A.
The descending order in the BEX indicator in hotel company B in the period from 2012
to 2014 points to its weak sustainability (Metaxas et al., 2016). The results provided by calculated BEX index indicate the need for improvement in the area of leadership excellence,
production and supply chain, strategic agility, partnerships and intellectual capital in order
to increase the business excellence (Metaxas et al., 2016) within all three observed hotel
companies. The results of the last given parameter Value for money, which in all observed
companies can be described as very good, point to additional improvements that can be
applied in conjunction with better guest satisfaction. In order to create the sustainable
brand loyalty, the design of a guest satisfaction surveying programme has to be linked to
cost-benefit analysis as well (Brlečić Valčić & Bagarić, 2015).
The fuzzy logic-based growth-development synchronisation coefficients calculated for
each year in the observed period and for the five-year averages are presented in Table 7
and Figure 10.
Presented results indicate the need for additional improvements in the area of defining
core competencies and skills, defining the core product or service, determination of the
relationship between core competencies and skills, adjustment of the core competencies,
and putting in place a strategic process for managing core competencies for all observed
companies (Table 2).
Table 7. Growth-development synchronisation coefficients for analysed hotel companies.
G&D Coefficient in 2010
G&D Coefficient in 2011
G&D Coefficient in 2012
G&D Coefficient in 2013
G&D Coefficient in 2014
G&D Coefficient in 2010–2014
Hotel Company A
2.32
2.37
2.76
2.91
3.01
2.72
Hotel Company B
2.38
2.47
2.69
2.55
2.53
2.59
Hotel Company C
2.23
2.68
2.78
3.06
2.66
2.67
Source: Authors.
Figure 10. The fuzzy logic-based growth-development synchronisation coefficient values for hotel
companies A, B and C. Source: Authors.
1650 S. BRLEČIĆ VALČIĆ AND L. BAGARIĆ
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The results also show that the hotel company A has the best strategy and the best
growth-development synchronisation. Through continued investment in revenue management and business organisation, this hotel company has good prospects of achieving
excellent performance, thus ensuring stable business operations in the long term.
The hotel company B achieved good results in G&D coefficient growth in the period
2010–2012, but after that time its G&D coefficient begins to drop rapidly. To ensure stable
performance in the long-term period, it will need to make much greater investments in
revenue management and business organisation.
The hotel company C is in a similar situation. After steadily growing in the period 2010–
2013, the company’s G&D coefficient begins to plummet, primarily because of low revenue
growth in 2014. To achieve higher revenue growth and, in turn, improve other financial
results, the company should increase considerably its investment in revenue management.
5. Conclusion
The results obtained in this research provide the evidence by which hotel companies may
determine important information using a proposed conceptual model for synchronising
growth and development strategies which can consequently reveal deficiencies in their business processes. These deficiencies can have a significant impact on business development
in the context of a stable and sustainable growth.
Previous findings showed that proper selection of business strategies can be considered
as a vital determinant both in defining the company and in establishing and forecasting
value creation and preservation. Mentioned categories are closely tied to long-term company
operations and expected business and financial results. Hotel companies need to examine
the connections between business operations, organisational structures, management and
strategies. Considering the relationship between core competencies, skills, performance and
corporate growth, the values and behaviours that contribute to the sustainable business of
an organisation can be established. Furthermore, in order to reach a sustainable business,
strategies have to be focused on enhancing customer value by efficient customer management and with expanding revenue opportunities. For that reason it may be said that good
revenue management is a crucial precondition to the strategic benchmarking for value
process. Effective revenue management helps to generate greater revenue and greater profitability which are preconditions to the long-term growth and development of a company
and the building of long-term customer relationship that is very important in hotel industry.
The guidelines from previous research were used as an orientation in selecting the
parameters that enable monitoring of business processes for the purpose of synchronisation of growth and development strategies in hotel companies. Asset turnover coefficient
was proposed for monitoring the required improvements in revenue management, cost
leadership strategy, productive as set utilisation, investment management, working capital
management and management of long-term assets. EBITDA / asset ratio was selected as
an appropriate in examination of the effective asset utilisation, operating processes and
management of operating expenses. On the other hand, the use of Net income / equity ratio
provides information about the necessary improvements in terms of adjustments to industry
conditions, competitive strategy analysis, operating management, investment management
and liability management. BEX index provides information on business excellence and the
necessary investments in the leadership excellence, production and supply chain, strategic
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ECONOMIC RESEARCH-EKONOMSKA ISTRAŽIVANJA 1651
agility, continuous improvement, partnerships and intellectual capital. The last parameter
Value for money was selected for testing guest satisfaction in the context of the examination
of their intention to regularly buy products and services thus providing a flow of revenue
which the management can transform into profit, free cash flow or use to attract investors.
Deployment of the growth-development synchronisation coefficient based on fuzzy logic
TOPSIS approach and design of a conceptual model for synchronising growth and development strategies in the hotel industry can be highlighted as the most significant contribution of this research. By using fuzzy logic TOPSIS approach the appropriate judgement
process was enabled, as well as creation of reasonable values for the weighting factors. In this
way, the proposed G&D coefficient presents an effective and reliable indicator of required
investments in business operations in order to ensure stable and sustainable growth of hotel
companies in future periods.
However, the research does have some limitations and these are particularly related to
the application possibilities of the proposed model. Namely, the proposed model is created
and tuned only for hotel companies and is tested only for hotel companies in Croatia. This
is particularly important in terms of input variable ranges that can vary and therefore it
is hard to find generalised ranges that can cover all possibilities. Thus, as with any fuzzy
based model, it is hard to ensure complete generality of the model, but on the other hand,
the tuning and adaptation of this model, if necessary, is relatively simple and this should
be emphasised as a very convenient advantage of this approach.
With respect to the aforementioned limitations and as a part of a future research, appropriate sensitivity and uncertainty analysis is recommended to be done, particularly on how
various values and ranges of input variables affect the G&D coefficient value. Additional
future research could also include certain modifications of the proposed model in order that
it can be implemented and used within any other industry sector beside the hotel industry.
Acknowledgement
This work was supported by the Croatian Science Foundation under the project 6558 Business and
Personal Insolvency and University of Rijeka under the project ZP UNIRI 1/15 Creating of a Tourism
Product Club with the Aim of Repositioning a Tourist Destination.
Disclosure statement
No potential conflict of interest was reported by the authors.
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